Antibacterial activity of sugar maple autumn‐shed leaf extract: Identification of the active compound
Bibliographic record
Abstract
Abstract Ethanolic crude extract prepared from autumn‐shed leaves of sugar maple ( Acer saccharum Marsh.) was recently shown to have antibacterial activity against Pseudomonas cichorii and Xanthomonas campestris pv. vitians , two bacteria causing diseases in lettuce production. In this study, antibacterial activity of sugar maple autumn‐shed leaves (SMASL) extract was further investigated. SMASL ethanolic crude extract was fractionated using HPLC system and geraniin was identified as the antibacterial compound by UPLC/Q‐Tof‐MS system. Geraniin, an ellagitannin, was then purified from SMASL crude extract using a glass chromatographic C18‐reversed phase silica gel column (purification Step 1) and a semi‐preparative HPLC system equipped with 5 μm XTerra Prep MS C18 column (purification Step 2). Minimal inhibitory concentrations (MICs) and minimal bactericidal concentrations (MBCs) of purified geraniin (purity of 96%) against P. cichorii and X. campestris pv. vitians were determined. X. campestris pv. vitians (MIC of 0.024 mg ml −1 and MBC of 3.125 mg ml −1 ) was more sensitive to geraniin than P. cichorii (MIC of 0.781 mg ml −1 and MBC of 6.25 mg ml −1 ). In the present study, geraniin is reported for the first time as the main antibacterial compound present in SMASL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".